Curso: Introducción al software R para Ciencia de Datos en Salud
XXXVI CCN Huancayo - SOCIMEP 2022
Percy Soto-Becerra M.D., M.Sc(c)
Investigador Externo, Universidad Continental, Huancayo
@github/psotob91
Agosto 11, 2022
Fuente: Sesion2-Introduccion a R y RStudio. Andre Valle y Steev Loyola Enlace
Esto de aquí:
Se puede simplificar a esto:
dplyr es una ‘suite’ de funciones para manipular dato id time treat age race married married2 procedence weight height
1 1 0 1 33 Mestiza 1 0 Callao 59.0 1.4
2 1 1 1 32 Mestiza 1 0 Callao 59.9 1.3
3 2 0 3 27 Mestiza 1 0 Santa Anita 62.0 1.5
4 2 1 3 27 Mestiza 1 0 Santa Anita 62.1 1.6
5 3 0 2 25 Mestiza 1 0 Callao 62.0 1.6
6 3 1 2 25 Mestiza 1 0 Callao 60.0 1.6
7 4 0 2 37 Mestiza 2 0 Callao 60.9 1.5
8 4 1 2 38 Mestiza 2 0 Callao 61.4 1.5
9 5 0 1 31 Mestiza 1 0 La Molina 64.0 1.5
10 5 1 1 32 Mestiza 1 0 La Molina 58.1 1.6
11 6 0 1 38 Mestiza 5 1 Los Olivos 54.5 1.5
12 6 1 1 38 Mestiza 5 1 Los Olivos 53.9 1.5
13 7 0 2 26 Mestiza 5 1 SMP 59.1 1.6
14 7 1 2 26 Mestiza 5 1 SMP 58.6 1.6
15 8 0 1 34 Mestiza 5 1 Carabayllo 64.0 1.5
16 8 1 1 34 Mestiza 5 1 Carabayllo 59.0 1.5
17 9 0 3 30 Mestiza 1 0 SMP 61.0 1.6
18 9 1 3 30 Mestiza 1 0 SMP 63.1 1.7
19 10 0 1 38 Mestiza 1 0 Pueblo Libre 56.1 1.7
20 10 1 1 38 Mestiza 1 0 Pueblo Libre 54.9 1.5
21 11 0 3 37 Mestiza 5 1 El Agustino 72.0 1.6
22 11 1 3 36 Mestiza 5 1 El Agustino NA NA
23 12 0 3 33 Mestiza 1 0 Lince 68.0 1.5
24 12 1 3 33 Mestiza 1 0 Lince 68.0 1.5
25 13 0 3 25 Mestiza 5 1 Callao 48.5 1.5
26 13 1 3 25 Mestiza 5 1 Callao 54.0 1.5
27 14 0 1 33 Mestiza 1 0 Surquillo 65.0 1.6
28 14 1 1 33 Mestiza 1 0 Surquillo 64.5 1.6
29 15 0 1 37 Mestiza 5 1 Comas 50.5 1.4
30 15 1 1 37 Mestiza 5 1 Comas 50.1 1.5
31 16 0 2 30 Mestiza 1 0 Los Olivos 56.0 1.5
32 16 1 2 30 Mestiza 1 0 Los Olivos 55.9 1.5
33 17 0 1 40 Mestiza 3 0 Surquillo 65.0 1.6
34 17 1 1 40 Mestiza 3 0 Surquillo 65.0 1.6
35 18 0 1 36 Mestiza 5 1 Miraflores 70.0 1.6
36 18 1 1 36 Mestiza 5 1 Miraflores 71.0 1.7
37 19 0 3 35 Mestiza 1 0 Comas 52.1 1.6
38 19 1 3 35 Mestiza 1 0 Comas 53.0 1.5
39 20 0 1 22 Mestiza 1 0 Surquillo 59.0 1.5
40 20 1 1 22 Mestiza 1 0 Surquillo 59.0 1.5
41 21 0 1 29 Mestiza 5 1 SJL 56.9 1.4
42 21 1 1 29 Mestiza 5 1 SJL 57.1 1.5
43 22 0 2 41 Mestiza 1 0 Chorrillos 64.0 1.5
44 22 1 2 41 Mestiza 1 0 Chorrillos 63.0 1.5
45 23 0 2 27 Mestiza 1 0 Los Olivos 52.0 1.5
46 23 1 2 27 Mestiza 1 0 Los Olivos 51.0 1.5
47 24 0 2 25 Mestiza 5 1 Los Olivos 64.0 1.5
48 24 1 2 25 Mestiza 5 1 Los Olivos 64.0 1.5
49 25 0 1 37 Mestiza 5 1 San Borja 58.1 1.6
50 25 1 1 37 Mestiza 5 1 San Borja 54.0 1.6
51 26 0 1 32 Mestiza 5 1 Chosica 72.1 1.7
52 26 1 1 32 Mestiza 5 1 Chosica 68.5 1.6
53 27 0 2 31 Mestiza 1 0 San Borja 54.0 1.5
54 27 1 2 31 Mestiza 1 0 San Borja 55.0 1.5
55 28 0 3 40 Mestiza 5 1 SJL 81.5 1.6
56 28 1 3 40 Mestiza 5 1 SJL 70.1 1.6
57 29 0 2 23 Mestiza 1 0 SJL 49.0 1.6
58 29 1 2 23 Mestiza 1 0 SJL 50.1 1.6
59 30 0 2 31 Mestiza 5 1 Comas 54.9 1.5
60 30 1 2 31 Mestiza 5 1 Comas 56.0 1.5
61 31 0 2 38 Mestiza 1 0 VMT 65.0 1.5
62 31 1 2 38 Mestiza 1 0 VMT 65.0 1.5
63 32 0 3 38 Mestiza 5 1 SMP 59.0 1.5
64 32 1 3 38 Mestiza 5 1 SMP 58.0 1.5
65 33 0 3 41 Mestiza 5 1 SJL 74.0 1.7
66 33 1 3 41 Mestiza 5 1 SJL 74.5 1.7
67 34 0 2 40 Mestiza 1 0 Bre;a 72.6 1.5
68 34 1 2 40 Mestiza 1 0 Bre;a 76.0 1.6
69 35 0 3 30 Mestiza 5 1 Rimac 51.9 1.4
70 35 1 3 30 Mestiza 5 1 Rimac 53.0 1.4
71 36 0 2 39 Mestiza 1 0 Magdalena 81.0 1.5
72 36 1 2 39 Mestiza 1 0 Magdalena NA NA
73 37 0 3 30 Mestiza 1 0 El Agustino 51.0 1.5
74 37 1 3 30 Mestiza 1 0 El Agustino 51.4 1.4
75 38 0 3 37 Mestiza 5 1 Lince 62.0 1.5
76 38 1 3 37 Mestiza 5 1 Lince 62.5 1.5
77 39 0 3 23 Mestiza 1 0 Callao 56.0 1.5
78 39 1 3 23 Mestiza 1 0 Callao 56.0 1.5
79 40 0 1 20 Mestiza 1 0 VES 61.0 1.5
80 40 1 1 20 Mestiza 1 0 VES 60.0 1.5
81 41 0 2 31 Mestiza 5 1 Lince 91.9 1.5
82 41 1 2 31 Mestiza 5 1 Lince 92.1 1.7
83 42 0 3 39 Mestiza 5 1 Los Olivos 55.0 1.6
84 42 1 3 39 Mestiza 5 1 Los Olivos 56.0 1.6
85 43 0 1 36 Mestiza 1 0 Villa el Salvador 56.9 1.4
86 43 1 1 36 Mestiza 1 0 Villa el Salvador 57.6 1.6
87 44 0 2 28 Mestiza 5 1 Lince 55.9 1.4
88 44 1 2 28 Mestiza 5 1 Lince 57.0 1.6
89 45 0 1 35 Mestiza 5 1 Los Olivos 78.1 1.7
90 45 1 1 35 Mestiza 5 1 Los Olivos NA NA
91 46 0 3 36 Mestiza 5 1 El Agustino 65.1 1.7
92 46 1 3 36 Mestiza 5 1 El Agustino 65.5 1.7
93 47 0 3 35 Mestiza 1 0 Magdalena 54.0 1.5
94 47 1 3 35 Mestiza 1 0 Magdalena 53.0 1.5
95 48 0 1 27 Mestiza 1 0 Callao 55.9 1.4
96 48 1 1 27 Mestiza 1 0 Callao 54.9 1.5
97 49 0 3 35 Mestiza 5 1 Comas 57.0 1.4
98 49 1 3 35 Mestiza 5 1 Comas 56.8 1.5
99 50 0 3 28 Mestiza 5 1 VMT 73.1 1.6
100 50 1 3 28 Mestiza 5 1 VMT 71.7 1.4
101 51 0 2 41 Mestiza 5 1 Surco 65.0 1.5
102 51 1 2 41 Mestiza 5 1 Surco 64.9 1.5
103 52 0 2 34 Mestiza 4 1 SMP 59.0 1.6
104 52 1 2 34 Mestiza 4 1 SMP 54.0 1.5
105 53 0 3 33 Mestiza 1 0 La Molina 64.9 1.4
106 53 1 3 33 Mestiza 1 0 La Molina 66.0 1.5
e2 lh fsh prog
1 87.30 3.28 1.95 14.20
2 210.05 26.85 8.83 12.95
3 169.01 6.34 4.32 0.50
4 99.91 5.77 1.70 9.61
5 78.76 11.86 2.81 10.46
6 155.04 10.14 4.51 5.04
7 40.99 4.57 3.81 4.64
8 109.03 7.29 2.39 11.73
9 43.01 7.81 2.01 15.11
10 56.05 9.15 3.66 11.46
11 36.25 2.89 3.86 10.85
12 44.67 5.87 3.09 12.03
13 91.28 3.25 4.77 9.54
14 91.34 3.31 4.83 9.60
15 65.65 6.08 2.11 13.65
16 134.04 3.99 1.53 9.53
17 49.58 10.88 4.04 0.55
18 46.28 17.58 4.65 0.08
19 114.88 12.28 1.58 8.73
20 71.47 1.57 2.40 3.68
21 66.38 5.38 5.31 13.58
22 110.32 32.02 3.55 19.10
23 403.99 28.69 3.53 21.89
24 178.96 7.02 1.67 16.46
25 73.80 4.30 2.86 14.80
26 130.15 15.85 5.78 6.95
27 47.35 4.56 3.44 4.84
28 59.20 6.15 2.06 10.00
29 143.09 92.89 1.98 23.29
30 110.18 8.28 5.26 5.82
31 80.99 7.27 5.81 0.69
32 73.12 3.82 3.55 3.80
33 91.76 5.35 2.25 6.59
34 73.00 5.37 2.10 11.20
35 131.00 19.20 1.74 11.20
36 261.03 49.33 2.17 14.63
37 164.96 6.48 1.89 9.69
38 165.02 6.54 1.95 9.75
39 98.95 5.68 3.17 0.98
40 33.15 3.45 5.39 12.65
41 71.61 6.54 1.94 9.37
42 29.55 1.02 6.72 0.41
43 91.41 1.81 9.13 0.55
44 111.98 32.58 21.18 0.22
45 87.62 3.96 5.34 9.50
46 93.67 3.19 5.62 7.25
47 158.96 5.75 3.83 12.26
48 120.03 13.13 4.66 3.45
49 66.97 1.46 4.68 2.94
50 93.10 35.40 6.80 2.21
51 116.97 1.58 3.39 2.93
52 21.15 1.85 3.50 5.88
53 40.44 6.03 2.29 10.94
54 140.00 3.76 2.63 9.77
55 104.09 4.77 2.89 10.99
56 150.07 8.07 4.37 12.02
57 59.55 7.01 4.06 2.72
58 129.97 2.94 1.55 16.57
59 96.97 5.95 5.42 3.92
60 160.40 24.10 2.03 7.58
61 48.53 10.33 3.60 0.14
62 124.00 8.26 6.43 10.90
63 108.94 3.27 3.45 10.04
64 122.97 5.63 5.37 9.79
65 56.65 12.95 5.18 14.45
66 219.99 15.09 7.18 12.99
67 150.06 49.16 1.26 28.46
68 170.49 46.19 2.61 11.30
69 85.74 3.54 1.83 4.58
70 46.48 3.38 4.65 4.21
71 109.87 2.95 3.61 8.64
72 103.95 2.06 1.57 8.99
73 76.06 2.81 3.92 5.67
74 45.26 1.17 2.41 5.86
75 26.21 7.50 3.71 20.51
76 81.91 3.02 1.99 10.41
77 58.90 2.24 1.57 10.90
78 75.49 2.64 7.96 0.91
79 98.62 2.42 4.51 5.97
80 267.99 2.39 0.82 1.47
81 45.39 5.47 4.11 9.58
82 58.67 14.77 3.67 3.71
83 48.53 3.03 2.33 21.53
84 21.40 1.98 4.18 0.63
85 132.01 6.74 8.91 9.96
86 39.65 13.25 5.41 0.26
87 139.94 61.44 5.43 12.64
88 27.29 4.31 4.00 9.04
89 122.98 4.95 4.38 21.48
90 366.07 14.87 5.49 7.42
91 386.97 14.77 1.93 9.39
92 91.74 5.64 5.25 9.43
93 77.89 4.99 1.28 7.09
94 77.32 24.92 4.63 7.90
95 185.02 4.28 3.72 5.79
96 28.87 17.47 12.07 1.45
97 31.49 3.15 2.33 20.69
98 110.16 12.16 3.96 11.54
99 121.67 23.17 1.57 13.37
100 217.02 4.27 2.66 1.47
101 47.80 14.90 1.78 15.00
102 120.17 21.07 4.30 16.29
103 289.00 60.20 9.95 11.80
104 75.78 4.38 14.23 9.96
105 136.89 3.63 2.01 10.69
106 217.00 4.26 5.36 0.20
[1] id time treat age race married
[7] married2 procedence weight height e2 lh
[13] fsh prog
<0 rows> (or 0-length row.names)
Y que sean placebo: [1] id time treat age race married
[7] married2 procedence weight height e2 lh
[13] fsh prog
<0 rows> (or 0-length row.names)
O que sean placebo: [1] id time treat age race married
[7] married2 procedence weight height e2 lh
[13] fsh prog
<0 rows> (or 0-length row.names)
id time treat age race married married2 procedence weight height
1 4 0 2 37 Mestiza 2 0 Callao 60.9 1.5
2 4 1 2 38 Mestiza 2 0 Callao 61.4 1.5
3 6 0 1 38 Mestiza 5 1 Los Olivos 54.5 1.5
4 6 1 1 38 Mestiza 5 1 Los Olivos 53.9 1.5
5 8 0 1 34 Mestiza 5 1 Carabayllo 64.0 1.5
6 8 1 1 34 Mestiza 5 1 Carabayllo 59.0 1.5
7 10 0 1 38 Mestiza 1 0 Pueblo Libre 56.1 1.7
8 10 1 1 38 Mestiza 1 0 Pueblo Libre 54.9 1.5
9 11 0 3 37 Mestiza 5 1 El Agustino 72.0 1.6
10 11 1 3 36 Mestiza 5 1 El Agustino NA NA
11 15 0 1 37 Mestiza 5 1 Comas 50.5 1.4
12 15 1 1 37 Mestiza 5 1 Comas 50.1 1.5
13 18 0 1 36 Mestiza 5 1 Miraflores 70.0 1.6
14 18 1 1 36 Mestiza 5 1 Miraflores 71.0 1.7
15 19 0 3 35 Mestiza 1 0 Comas 52.1 1.6
16 19 1 3 35 Mestiza 1 0 Comas 53.0 1.5
17 25 0 1 37 Mestiza 5 1 San Borja 58.1 1.6
18 25 1 1 37 Mestiza 5 1 San Borja 54.0 1.6
19 31 0 2 38 Mestiza 1 0 VMT 65.0 1.5
20 31 1 2 38 Mestiza 1 0 VMT 65.0 1.5
21 32 0 3 38 Mestiza 5 1 SMP 59.0 1.5
22 32 1 3 38 Mestiza 5 1 SMP 58.0 1.5
23 38 0 3 37 Mestiza 5 1 Lince 62.0 1.5
24 38 1 3 37 Mestiza 5 1 Lince 62.5 1.5
25 43 0 1 36 Mestiza 1 0 Villa el Salvador 56.9 1.4
26 43 1 1 36 Mestiza 1 0 Villa el Salvador 57.6 1.6
27 45 0 1 35 Mestiza 5 1 Los Olivos 78.1 1.7
28 45 1 1 35 Mestiza 5 1 Los Olivos NA NA
29 46 0 3 36 Mestiza 5 1 El Agustino 65.1 1.7
30 46 1 3 36 Mestiza 5 1 El Agustino 65.5 1.7
31 47 0 3 35 Mestiza 1 0 Magdalena 54.0 1.5
32 47 1 3 35 Mestiza 1 0 Magdalena 53.0 1.5
33 49 0 3 35 Mestiza 5 1 Comas 57.0 1.4
34 49 1 3 35 Mestiza 5 1 Comas 56.8 1.5
35 52 0 2 34 Mestiza 4 1 SMP 59.0 1.6
36 52 1 2 34 Mestiza 4 1 SMP 54.0 1.5
e2 lh fsh prog
1 40.99 4.57 3.81 4.64
2 109.03 7.29 2.39 11.73
3 36.25 2.89 3.86 10.85
4 44.67 5.87 3.09 12.03
5 65.65 6.08 2.11 13.65
6 134.04 3.99 1.53 9.53
7 114.88 12.28 1.58 8.73
8 71.47 1.57 2.40 3.68
9 66.38 5.38 5.31 13.58
10 110.32 32.02 3.55 19.10
11 143.09 92.89 1.98 23.29
12 110.18 8.28 5.26 5.82
13 131.00 19.20 1.74 11.20
14 261.03 49.33 2.17 14.63
15 164.96 6.48 1.89 9.69
16 165.02 6.54 1.95 9.75
17 66.97 1.46 4.68 2.94
18 93.10 35.40 6.80 2.21
19 48.53 10.33 3.60 0.14
20 124.00 8.26 6.43 10.90
21 108.94 3.27 3.45 10.04
22 122.97 5.63 5.37 9.79
23 26.21 7.50 3.71 20.51
24 81.91 3.02 1.99 10.41
25 132.01 6.74 8.91 9.96
26 39.65 13.25 5.41 0.26
27 122.98 4.95 4.38 21.48
28 366.07 14.87 5.49 7.42
29 386.97 14.77 1.93 9.39
30 91.74 5.64 5.25 9.43
31 77.89 4.99 1.28 7.09
32 77.32 24.92 4.63 7.90
33 31.49 3.15 2.33 20.69
34 110.16 12.16 3.96 11.54
35 289.00 60.20 9.95 11.80
36 75.78 4.38 14.23 9.96
Opción válida pero ineficiente:
datos_fase1 %>%
filter(procedence == "Santa Anita" | procedence == "Callao" | procedence == "SMP" | procedence == "Carabayllo") id time treat age race married married2 procedence weight height e2
1 1 0 1 33 Mestiza 1 0 Callao 59.0 1.4 87.30
2 1 1 1 32 Mestiza 1 0 Callao 59.9 1.3 210.05
3 2 0 3 27 Mestiza 1 0 Santa Anita 62.0 1.5 169.01
4 2 1 3 27 Mestiza 1 0 Santa Anita 62.1 1.6 99.91
5 3 0 2 25 Mestiza 1 0 Callao 62.0 1.6 78.76
6 3 1 2 25 Mestiza 1 0 Callao 60.0 1.6 155.04
7 4 0 2 37 Mestiza 2 0 Callao 60.9 1.5 40.99
8 4 1 2 38 Mestiza 2 0 Callao 61.4 1.5 109.03
9 7 0 2 26 Mestiza 5 1 SMP 59.1 1.6 91.28
10 7 1 2 26 Mestiza 5 1 SMP 58.6 1.6 91.34
11 8 0 1 34 Mestiza 5 1 Carabayllo 64.0 1.5 65.65
12 8 1 1 34 Mestiza 5 1 Carabayllo 59.0 1.5 134.04
13 9 0 3 30 Mestiza 1 0 SMP 61.0 1.6 49.58
14 9 1 3 30 Mestiza 1 0 SMP 63.1 1.7 46.28
15 13 0 3 25 Mestiza 5 1 Callao 48.5 1.5 73.80
16 13 1 3 25 Mestiza 5 1 Callao 54.0 1.5 130.15
17 32 0 3 38 Mestiza 5 1 SMP 59.0 1.5 108.94
18 32 1 3 38 Mestiza 5 1 SMP 58.0 1.5 122.97
19 39 0 3 23 Mestiza 1 0 Callao 56.0 1.5 58.90
20 39 1 3 23 Mestiza 1 0 Callao 56.0 1.5 75.49
21 48 0 1 27 Mestiza 1 0 Callao 55.9 1.4 185.02
22 48 1 1 27 Mestiza 1 0 Callao 54.9 1.5 28.87
23 52 0 2 34 Mestiza 4 1 SMP 59.0 1.6 289.00
24 52 1 2 34 Mestiza 4 1 SMP 54.0 1.5 75.78
lh fsh prog
1 3.28 1.95 14.20
2 26.85 8.83 12.95
3 6.34 4.32 0.50
4 5.77 1.70 9.61
5 11.86 2.81 10.46
6 10.14 4.51 5.04
7 4.57 3.81 4.64
8 7.29 2.39 11.73
9 3.25 4.77 9.54
10 3.31 4.83 9.60
11 6.08 2.11 13.65
12 3.99 1.53 9.53
13 10.88 4.04 0.55
14 17.58 4.65 0.08
15 4.30 2.86 14.80
16 15.85 5.78 6.95
17 3.27 3.45 10.04
18 5.63 5.37 9.79
19 2.24 1.57 10.90
20 2.64 7.96 0.91
21 4.28 3.72 5.79
22 17.47 12.07 1.45
23 60.20 9.95 11.80
24 4.38 14.23 9.96
Opción válida y eficiente:
id time treat age race married married2 procedence weight height e2
1 1 0 1 33 Mestiza 1 0 Callao 59.0 1.4 87.30
2 1 1 1 32 Mestiza 1 0 Callao 59.9 1.3 210.05
3 2 0 3 27 Mestiza 1 0 Santa Anita 62.0 1.5 169.01
4 2 1 3 27 Mestiza 1 0 Santa Anita 62.1 1.6 99.91
5 3 0 2 25 Mestiza 1 0 Callao 62.0 1.6 78.76
6 3 1 2 25 Mestiza 1 0 Callao 60.0 1.6 155.04
7 4 0 2 37 Mestiza 2 0 Callao 60.9 1.5 40.99
8 4 1 2 38 Mestiza 2 0 Callao 61.4 1.5 109.03
9 7 0 2 26 Mestiza 5 1 SMP 59.1 1.6 91.28
10 7 1 2 26 Mestiza 5 1 SMP 58.6 1.6 91.34
11 8 0 1 34 Mestiza 5 1 Carabayllo 64.0 1.5 65.65
12 8 1 1 34 Mestiza 5 1 Carabayllo 59.0 1.5 134.04
13 9 0 3 30 Mestiza 1 0 SMP 61.0 1.6 49.58
14 9 1 3 30 Mestiza 1 0 SMP 63.1 1.7 46.28
15 13 0 3 25 Mestiza 5 1 Callao 48.5 1.5 73.80
16 13 1 3 25 Mestiza 5 1 Callao 54.0 1.5 130.15
17 32 0 3 38 Mestiza 5 1 SMP 59.0 1.5 108.94
18 32 1 3 38 Mestiza 5 1 SMP 58.0 1.5 122.97
19 39 0 3 23 Mestiza 1 0 Callao 56.0 1.5 58.90
20 39 1 3 23 Mestiza 1 0 Callao 56.0 1.5 75.49
21 48 0 1 27 Mestiza 1 0 Callao 55.9 1.4 185.02
22 48 1 1 27 Mestiza 1 0 Callao 54.9 1.5 28.87
23 52 0 2 34 Mestiza 4 1 SMP 59.0 1.6 289.00
24 52 1 2 34 Mestiza 4 1 SMP 54.0 1.5 75.78
lh fsh prog
1 3.28 1.95 14.20
2 26.85 8.83 12.95
3 6.34 4.32 0.50
4 5.77 1.70 9.61
5 11.86 2.81 10.46
6 10.14 4.51 5.04
7 4.57 3.81 4.64
8 7.29 2.39 11.73
9 3.25 4.77 9.54
10 3.31 4.83 9.60
11 6.08 2.11 13.65
12 3.99 1.53 9.53
13 10.88 4.04 0.55
14 17.58 4.65 0.08
15 4.30 2.86 14.80
16 15.85 5.78 6.95
17 3.27 3.45 10.04
18 5.63 5.37 9.79
19 2.24 1.57 10.90
20 2.64 7.96 0.91
21 4.28 3.72 5.79
22 17.47 12.07 1.45
23 60.20 9.95 11.80
24 4.38 14.23 9.96
La función select() selecciona columnas
El signo - permite elegir qué columnas eliminar.
A veces es mejor llamarla usando: dplyr::select() debido a que otros paquetes también tienen una función con el mismo nombre select()
id time treat age race married married2 procedence weight height
1 1 0 1 33 Mestiza 1 0 Callao 59.0 1.4
2 1 1 1 32 Mestiza 1 0 Callao 59.9 1.3
3 2 0 3 27 Mestiza 1 0 Santa Anita 62.0 1.5
4 2 1 3 27 Mestiza 1 0 Santa Anita 62.1 1.6
5 3 0 2 25 Mestiza 1 0 Callao 62.0 1.6
6 3 1 2 25 Mestiza 1 0 Callao 60.0 1.6
7 4 0 2 37 Mestiza 2 0 Callao 60.9 1.5
8 4 1 2 38 Mestiza 2 0 Callao 61.4 1.5
9 5 0 1 31 Mestiza 1 0 La Molina 64.0 1.5
10 5 1 1 32 Mestiza 1 0 La Molina 58.1 1.6
11 6 0 1 38 Mestiza 5 1 Los Olivos 54.5 1.5
12 6 1 1 38 Mestiza 5 1 Los Olivos 53.9 1.5
13 7 0 2 26 Mestiza 5 1 SMP 59.1 1.6
14 7 1 2 26 Mestiza 5 1 SMP 58.6 1.6
15 8 0 1 34 Mestiza 5 1 Carabayllo 64.0 1.5
16 8 1 1 34 Mestiza 5 1 Carabayllo 59.0 1.5
17 9 0 3 30 Mestiza 1 0 SMP 61.0 1.6
18 9 1 3 30 Mestiza 1 0 SMP 63.1 1.7
19 10 0 1 38 Mestiza 1 0 Pueblo Libre 56.1 1.7
20 10 1 1 38 Mestiza 1 0 Pueblo Libre 54.9 1.5
21 11 0 3 37 Mestiza 5 1 El Agustino 72.0 1.6
22 11 1 3 36 Mestiza 5 1 El Agustino NA NA
23 12 0 3 33 Mestiza 1 0 Lince 68.0 1.5
24 12 1 3 33 Mestiza 1 0 Lince 68.0 1.5
25 13 0 3 25 Mestiza 5 1 Callao 48.5 1.5
26 13 1 3 25 Mestiza 5 1 Callao 54.0 1.5
27 14 0 1 33 Mestiza 1 0 Surquillo 65.0 1.6
28 14 1 1 33 Mestiza 1 0 Surquillo 64.5 1.6
29 15 0 1 37 Mestiza 5 1 Comas 50.5 1.4
30 15 1 1 37 Mestiza 5 1 Comas 50.1 1.5
31 16 0 2 30 Mestiza 1 0 Los Olivos 56.0 1.5
32 16 1 2 30 Mestiza 1 0 Los Olivos 55.9 1.5
33 17 0 1 40 Mestiza 3 0 Surquillo 65.0 1.6
34 17 1 1 40 Mestiza 3 0 Surquillo 65.0 1.6
35 18 0 1 36 Mestiza 5 1 Miraflores 70.0 1.6
36 18 1 1 36 Mestiza 5 1 Miraflores 71.0 1.7
37 19 0 3 35 Mestiza 1 0 Comas 52.1 1.6
38 19 1 3 35 Mestiza 1 0 Comas 53.0 1.5
39 20 0 1 22 Mestiza 1 0 Surquillo 59.0 1.5
40 20 1 1 22 Mestiza 1 0 Surquillo 59.0 1.5
41 21 0 1 29 Mestiza 5 1 SJL 56.9 1.4
42 21 1 1 29 Mestiza 5 1 SJL 57.1 1.5
43 22 0 2 41 Mestiza 1 0 Chorrillos 64.0 1.5
44 22 1 2 41 Mestiza 1 0 Chorrillos 63.0 1.5
45 23 0 2 27 Mestiza 1 0 Los Olivos 52.0 1.5
46 23 1 2 27 Mestiza 1 0 Los Olivos 51.0 1.5
47 24 0 2 25 Mestiza 5 1 Los Olivos 64.0 1.5
48 24 1 2 25 Mestiza 5 1 Los Olivos 64.0 1.5
49 25 0 1 37 Mestiza 5 1 San Borja 58.1 1.6
50 25 1 1 37 Mestiza 5 1 San Borja 54.0 1.6
51 26 0 1 32 Mestiza 5 1 Chosica 72.1 1.7
52 26 1 1 32 Mestiza 5 1 Chosica 68.5 1.6
53 27 0 2 31 Mestiza 1 0 San Borja 54.0 1.5
54 27 1 2 31 Mestiza 1 0 San Borja 55.0 1.5
55 28 0 3 40 Mestiza 5 1 SJL 81.5 1.6
56 28 1 3 40 Mestiza 5 1 SJL 70.1 1.6
57 29 0 2 23 Mestiza 1 0 SJL 49.0 1.6
58 29 1 2 23 Mestiza 1 0 SJL 50.1 1.6
59 30 0 2 31 Mestiza 5 1 Comas 54.9 1.5
60 30 1 2 31 Mestiza 5 1 Comas 56.0 1.5
61 31 0 2 38 Mestiza 1 0 VMT 65.0 1.5
62 31 1 2 38 Mestiza 1 0 VMT 65.0 1.5
63 32 0 3 38 Mestiza 5 1 SMP 59.0 1.5
64 32 1 3 38 Mestiza 5 1 SMP 58.0 1.5
65 33 0 3 41 Mestiza 5 1 SJL 74.0 1.7
66 33 1 3 41 Mestiza 5 1 SJL 74.5 1.7
67 34 0 2 40 Mestiza 1 0 Bre;a 72.6 1.5
68 34 1 2 40 Mestiza 1 0 Bre;a 76.0 1.6
69 35 0 3 30 Mestiza 5 1 Rimac 51.9 1.4
70 35 1 3 30 Mestiza 5 1 Rimac 53.0 1.4
71 36 0 2 39 Mestiza 1 0 Magdalena 81.0 1.5
72 36 1 2 39 Mestiza 1 0 Magdalena NA NA
73 37 0 3 30 Mestiza 1 0 El Agustino 51.0 1.5
74 37 1 3 30 Mestiza 1 0 El Agustino 51.4 1.4
75 38 0 3 37 Mestiza 5 1 Lince 62.0 1.5
76 38 1 3 37 Mestiza 5 1 Lince 62.5 1.5
77 39 0 3 23 Mestiza 1 0 Callao 56.0 1.5
78 39 1 3 23 Mestiza 1 0 Callao 56.0 1.5
79 40 0 1 20 Mestiza 1 0 VES 61.0 1.5
80 40 1 1 20 Mestiza 1 0 VES 60.0 1.5
81 41 0 2 31 Mestiza 5 1 Lince 91.9 1.5
82 41 1 2 31 Mestiza 5 1 Lince 92.1 1.7
83 42 0 3 39 Mestiza 5 1 Los Olivos 55.0 1.6
84 42 1 3 39 Mestiza 5 1 Los Olivos 56.0 1.6
85 43 0 1 36 Mestiza 1 0 Villa el Salvador 56.9 1.4
86 43 1 1 36 Mestiza 1 0 Villa el Salvador 57.6 1.6
87 44 0 2 28 Mestiza 5 1 Lince 55.9 1.4
88 44 1 2 28 Mestiza 5 1 Lince 57.0 1.6
89 45 0 1 35 Mestiza 5 1 Los Olivos 78.1 1.7
90 45 1 1 35 Mestiza 5 1 Los Olivos NA NA
91 46 0 3 36 Mestiza 5 1 El Agustino 65.1 1.7
92 46 1 3 36 Mestiza 5 1 El Agustino 65.5 1.7
93 47 0 3 35 Mestiza 1 0 Magdalena 54.0 1.5
94 47 1 3 35 Mestiza 1 0 Magdalena 53.0 1.5
95 48 0 1 27 Mestiza 1 0 Callao 55.9 1.4
96 48 1 1 27 Mestiza 1 0 Callao 54.9 1.5
97 49 0 3 35 Mestiza 5 1 Comas 57.0 1.4
98 49 1 3 35 Mestiza 5 1 Comas 56.8 1.5
99 50 0 3 28 Mestiza 5 1 VMT 73.1 1.6
100 50 1 3 28 Mestiza 5 1 VMT 71.7 1.4
101 51 0 2 41 Mestiza 5 1 Surco 65.0 1.5
102 51 1 2 41 Mestiza 5 1 Surco 64.9 1.5
103 52 0 2 34 Mestiza 4 1 SMP 59.0 1.6
104 52 1 2 34 Mestiza 4 1 SMP 54.0 1.5
105 53 0 3 33 Mestiza 1 0 La Molina 64.9 1.4
106 53 1 3 33 Mestiza 1 0 La Molina 66.0 1.5
e2 lh fsh prog
1 87.30 3.28 1.95 14.20
2 210.05 26.85 8.83 12.95
3 169.01 6.34 4.32 0.50
4 99.91 5.77 1.70 9.61
5 78.76 11.86 2.81 10.46
6 155.04 10.14 4.51 5.04
7 40.99 4.57 3.81 4.64
8 109.03 7.29 2.39 11.73
9 43.01 7.81 2.01 15.11
10 56.05 9.15 3.66 11.46
11 36.25 2.89 3.86 10.85
12 44.67 5.87 3.09 12.03
13 91.28 3.25 4.77 9.54
14 91.34 3.31 4.83 9.60
15 65.65 6.08 2.11 13.65
16 134.04 3.99 1.53 9.53
17 49.58 10.88 4.04 0.55
18 46.28 17.58 4.65 0.08
19 114.88 12.28 1.58 8.73
20 71.47 1.57 2.40 3.68
21 66.38 5.38 5.31 13.58
22 110.32 32.02 3.55 19.10
23 403.99 28.69 3.53 21.89
24 178.96 7.02 1.67 16.46
25 73.80 4.30 2.86 14.80
26 130.15 15.85 5.78 6.95
27 47.35 4.56 3.44 4.84
28 59.20 6.15 2.06 10.00
29 143.09 92.89 1.98 23.29
30 110.18 8.28 5.26 5.82
31 80.99 7.27 5.81 0.69
32 73.12 3.82 3.55 3.80
33 91.76 5.35 2.25 6.59
34 73.00 5.37 2.10 11.20
35 131.00 19.20 1.74 11.20
36 261.03 49.33 2.17 14.63
37 164.96 6.48 1.89 9.69
38 165.02 6.54 1.95 9.75
39 98.95 5.68 3.17 0.98
40 33.15 3.45 5.39 12.65
41 71.61 6.54 1.94 9.37
42 29.55 1.02 6.72 0.41
43 91.41 1.81 9.13 0.55
44 111.98 32.58 21.18 0.22
45 87.62 3.96 5.34 9.50
46 93.67 3.19 5.62 7.25
47 158.96 5.75 3.83 12.26
48 120.03 13.13 4.66 3.45
49 66.97 1.46 4.68 2.94
50 93.10 35.40 6.80 2.21
51 116.97 1.58 3.39 2.93
52 21.15 1.85 3.50 5.88
53 40.44 6.03 2.29 10.94
54 140.00 3.76 2.63 9.77
55 104.09 4.77 2.89 10.99
56 150.07 8.07 4.37 12.02
57 59.55 7.01 4.06 2.72
58 129.97 2.94 1.55 16.57
59 96.97 5.95 5.42 3.92
60 160.40 24.10 2.03 7.58
61 48.53 10.33 3.60 0.14
62 124.00 8.26 6.43 10.90
63 108.94 3.27 3.45 10.04
64 122.97 5.63 5.37 9.79
65 56.65 12.95 5.18 14.45
66 219.99 15.09 7.18 12.99
67 150.06 49.16 1.26 28.46
68 170.49 46.19 2.61 11.30
69 85.74 3.54 1.83 4.58
70 46.48 3.38 4.65 4.21
71 109.87 2.95 3.61 8.64
72 103.95 2.06 1.57 8.99
73 76.06 2.81 3.92 5.67
74 45.26 1.17 2.41 5.86
75 26.21 7.50 3.71 20.51
76 81.91 3.02 1.99 10.41
77 58.90 2.24 1.57 10.90
78 75.49 2.64 7.96 0.91
79 98.62 2.42 4.51 5.97
80 267.99 2.39 0.82 1.47
81 45.39 5.47 4.11 9.58
82 58.67 14.77 3.67 3.71
83 48.53 3.03 2.33 21.53
84 21.40 1.98 4.18 0.63
85 132.01 6.74 8.91 9.96
86 39.65 13.25 5.41 0.26
87 139.94 61.44 5.43 12.64
88 27.29 4.31 4.00 9.04
89 122.98 4.95 4.38 21.48
90 366.07 14.87 5.49 7.42
91 386.97 14.77 1.93 9.39
92 91.74 5.64 5.25 9.43
93 77.89 4.99 1.28 7.09
94 77.32 24.92 4.63 7.90
95 185.02 4.28 3.72 5.79
96 28.87 17.47 12.07 1.45
97 31.49 3.15 2.33 20.69
98 110.16 12.16 3.96 11.54
99 121.67 23.17 1.57 13.37
100 217.02 4.27 2.66 1.47
101 47.80 14.90 1.78 15.00
102 120.17 21.07 4.30 16.29
103 289.00 60.20 9.95 11.80
104 75.78 4.38 14.23 9.96
105 136.89 3.63 2.01 10.69
106 217.00 4.26 5.36 0.20
datos_fase1_seleccionados <- datos_fase1 %>%
select(id, time, age, race, procedence)
datos_fase1_seleccionados id time age race procedence
1 1 0 33 Mestiza Callao
2 1 1 32 Mestiza Callao
3 2 0 27 Mestiza Santa Anita
4 2 1 27 Mestiza Santa Anita
5 3 0 25 Mestiza Callao
6 3 1 25 Mestiza Callao
7 4 0 37 Mestiza Callao
8 4 1 38 Mestiza Callao
9 5 0 31 Mestiza La Molina
10 5 1 32 Mestiza La Molina
11 6 0 38 Mestiza Los Olivos
12 6 1 38 Mestiza Los Olivos
13 7 0 26 Mestiza SMP
14 7 1 26 Mestiza SMP
15 8 0 34 Mestiza Carabayllo
16 8 1 34 Mestiza Carabayllo
17 9 0 30 Mestiza SMP
18 9 1 30 Mestiza SMP
19 10 0 38 Mestiza Pueblo Libre
20 10 1 38 Mestiza Pueblo Libre
21 11 0 37 Mestiza El Agustino
22 11 1 36 Mestiza El Agustino
23 12 0 33 Mestiza Lince
24 12 1 33 Mestiza Lince
25 13 0 25 Mestiza Callao
26 13 1 25 Mestiza Callao
27 14 0 33 Mestiza Surquillo
28 14 1 33 Mestiza Surquillo
29 15 0 37 Mestiza Comas
30 15 1 37 Mestiza Comas
31 16 0 30 Mestiza Los Olivos
32 16 1 30 Mestiza Los Olivos
33 17 0 40 Mestiza Surquillo
34 17 1 40 Mestiza Surquillo
35 18 0 36 Mestiza Miraflores
36 18 1 36 Mestiza Miraflores
37 19 0 35 Mestiza Comas
38 19 1 35 Mestiza Comas
39 20 0 22 Mestiza Surquillo
40 20 1 22 Mestiza Surquillo
41 21 0 29 Mestiza SJL
42 21 1 29 Mestiza SJL
43 22 0 41 Mestiza Chorrillos
44 22 1 41 Mestiza Chorrillos
45 23 0 27 Mestiza Los Olivos
46 23 1 27 Mestiza Los Olivos
47 24 0 25 Mestiza Los Olivos
48 24 1 25 Mestiza Los Olivos
49 25 0 37 Mestiza San Borja
50 25 1 37 Mestiza San Borja
51 26 0 32 Mestiza Chosica
52 26 1 32 Mestiza Chosica
53 27 0 31 Mestiza San Borja
54 27 1 31 Mestiza San Borja
55 28 0 40 Mestiza SJL
56 28 1 40 Mestiza SJL
57 29 0 23 Mestiza SJL
58 29 1 23 Mestiza SJL
59 30 0 31 Mestiza Comas
60 30 1 31 Mestiza Comas
61 31 0 38 Mestiza VMT
62 31 1 38 Mestiza VMT
63 32 0 38 Mestiza SMP
64 32 1 38 Mestiza SMP
65 33 0 41 Mestiza SJL
66 33 1 41 Mestiza SJL
67 34 0 40 Mestiza Bre;a
68 34 1 40 Mestiza Bre;a
69 35 0 30 Mestiza Rimac
70 35 1 30 Mestiza Rimac
71 36 0 39 Mestiza Magdalena
72 36 1 39 Mestiza Magdalena
73 37 0 30 Mestiza El Agustino
74 37 1 30 Mestiza El Agustino
75 38 0 37 Mestiza Lince
76 38 1 37 Mestiza Lince
77 39 0 23 Mestiza Callao
78 39 1 23 Mestiza Callao
79 40 0 20 Mestiza VES
80 40 1 20 Mestiza VES
81 41 0 31 Mestiza Lince
82 41 1 31 Mestiza Lince
83 42 0 39 Mestiza Los Olivos
84 42 1 39 Mestiza Los Olivos
85 43 0 36 Mestiza Villa el Salvador
86 43 1 36 Mestiza Villa el Salvador
87 44 0 28 Mestiza Lince
88 44 1 28 Mestiza Lince
89 45 0 35 Mestiza Los Olivos
90 45 1 35 Mestiza Los Olivos
91 46 0 36 Mestiza El Agustino
92 46 1 36 Mestiza El Agustino
93 47 0 35 Mestiza Magdalena
94 47 1 35 Mestiza Magdalena
95 48 0 27 Mestiza Callao
96 48 1 27 Mestiza Callao
97 49 0 35 Mestiza Comas
98 49 1 35 Mestiza Comas
99 50 0 28 Mestiza VMT
100 50 1 28 Mestiza VMT
101 51 0 41 Mestiza Surco
102 51 1 41 Mestiza Surco
103 52 0 34 Mestiza SMP
104 52 1 34 Mestiza SMP
105 53 0 33 Mestiza La Molina
106 53 1 33 Mestiza La Molina
id time age race
1 1 0 33 Mestiza
2 1 1 32 Mestiza
3 2 0 27 Mestiza
4 2 1 27 Mestiza
5 3 0 25 Mestiza
6 3 1 25 Mestiza
7 4 0 37 Mestiza
8 4 1 38 Mestiza
9 5 0 31 Mestiza
10 5 1 32 Mestiza
11 6 0 38 Mestiza
12 6 1 38 Mestiza
13 7 0 26 Mestiza
14 7 1 26 Mestiza
15 8 0 34 Mestiza
16 8 1 34 Mestiza
17 9 0 30 Mestiza
18 9 1 30 Mestiza
19 10 0 38 Mestiza
20 10 1 38 Mestiza
21 11 0 37 Mestiza
22 11 1 36 Mestiza
23 12 0 33 Mestiza
24 12 1 33 Mestiza
25 13 0 25 Mestiza
26 13 1 25 Mestiza
27 14 0 33 Mestiza
28 14 1 33 Mestiza
29 15 0 37 Mestiza
30 15 1 37 Mestiza
31 16 0 30 Mestiza
32 16 1 30 Mestiza
33 17 0 40 Mestiza
34 17 1 40 Mestiza
35 18 0 36 Mestiza
36 18 1 36 Mestiza
37 19 0 35 Mestiza
38 19 1 35 Mestiza
39 20 0 22 Mestiza
40 20 1 22 Mestiza
41 21 0 29 Mestiza
42 21 1 29 Mestiza
43 22 0 41 Mestiza
44 22 1 41 Mestiza
45 23 0 27 Mestiza
46 23 1 27 Mestiza
47 24 0 25 Mestiza
48 24 1 25 Mestiza
49 25 0 37 Mestiza
50 25 1 37 Mestiza
51 26 0 32 Mestiza
52 26 1 32 Mestiza
53 27 0 31 Mestiza
54 27 1 31 Mestiza
55 28 0 40 Mestiza
56 28 1 40 Mestiza
57 29 0 23 Mestiza
58 29 1 23 Mestiza
59 30 0 31 Mestiza
60 30 1 31 Mestiza
61 31 0 38 Mestiza
62 31 1 38 Mestiza
63 32 0 38 Mestiza
64 32 1 38 Mestiza
65 33 0 41 Mestiza
66 33 1 41 Mestiza
67 34 0 40 Mestiza
68 34 1 40 Mestiza
69 35 0 30 Mestiza
70 35 1 30 Mestiza
71 36 0 39 Mestiza
72 36 1 39 Mestiza
73 37 0 30 Mestiza
74 37 1 30 Mestiza
75 38 0 37 Mestiza
76 38 1 37 Mestiza
77 39 0 23 Mestiza
78 39 1 23 Mestiza
79 40 0 20 Mestiza
80 40 1 20 Mestiza
81 41 0 31 Mestiza
82 41 1 31 Mestiza
83 42 0 39 Mestiza
84 42 1 39 Mestiza
85 43 0 36 Mestiza
86 43 1 36 Mestiza
87 44 0 28 Mestiza
88 44 1 28 Mestiza
89 45 0 35 Mestiza
90 45 1 35 Mestiza
91 46 0 36 Mestiza
92 46 1 36 Mestiza
93 47 0 35 Mestiza
94 47 1 35 Mestiza
95 48 0 27 Mestiza
96 48 1 27 Mestiza
97 49 0 35 Mestiza
98 49 1 35 Mestiza
99 50 0 28 Mestiza
100 50 1 28 Mestiza
101 51 0 41 Mestiza
102 51 1 41 Mestiza
103 52 0 34 Mestiza
104 52 1 34 Mestiza
105 53 0 33 Mestiza
106 53 1 33 Mestiza
datos_fase1 %>%
filter(age > 35) %>% # Primero filtro por age
select(id, time, treat, race, age) # Luego me quedo solo con estas 5 columnas id time treat race age
1 4 0 2 Mestiza 37
2 4 1 2 Mestiza 38
3 6 0 1 Mestiza 38
4 6 1 1 Mestiza 38
5 10 0 1 Mestiza 38
6 10 1 1 Mestiza 38
7 11 0 3 Mestiza 37
8 11 1 3 Mestiza 36
9 15 0 1 Mestiza 37
10 15 1 1 Mestiza 37
11 17 0 1 Mestiza 40
12 17 1 1 Mestiza 40
13 18 0 1 Mestiza 36
14 18 1 1 Mestiza 36
15 22 0 2 Mestiza 41
16 22 1 2 Mestiza 41
17 25 0 1 Mestiza 37
18 25 1 1 Mestiza 37
19 28 0 3 Mestiza 40
20 28 1 3 Mestiza 40
21 31 0 2 Mestiza 38
22 31 1 2 Mestiza 38
23 32 0 3 Mestiza 38
24 32 1 3 Mestiza 38
25 33 0 3 Mestiza 41
26 33 1 3 Mestiza 41
27 34 0 2 Mestiza 40
28 34 1 2 Mestiza 40
29 36 0 2 Mestiza 39
30 36 1 2 Mestiza 39
31 38 0 3 Mestiza 37
32 38 1 3 Mestiza 37
33 42 0 3 Mestiza 39
34 42 1 3 Mestiza 39
35 43 0 1 Mestiza 36
36 43 1 1 Mestiza 36
37 46 0 3 Mestiza 36
38 46 1 3 Mestiza 36
39 51 0 2 Mestiza 41
40 51 1 2 Mestiza 41
mutate() crea nuevas columnas en base a otras.datos_fase1 %>%
select(id, weight, height) %>% # Nos quedamos con peso y talla
mutate(imc = weight / height ^ 2) # Creamos IMC en base a peso y talla id weight height imc
1 1 59.0 1.4 30.10204
2 1 59.9 1.3 35.44379
3 2 62.0 1.5 27.55556
4 2 62.1 1.6 24.25781
5 3 62.0 1.6 24.21875
6 3 60.0 1.6 23.43750
7 4 60.9 1.5 27.06667
8 4 61.4 1.5 27.28889
9 5 64.0 1.5 28.44444
10 5 58.1 1.6 22.69531
11 6 54.5 1.5 24.22222
12 6 53.9 1.5 23.95556
13 7 59.1 1.6 23.08594
14 7 58.6 1.6 22.89062
15 8 64.0 1.5 28.44444
16 8 59.0 1.5 26.22222
17 9 61.0 1.6 23.82812
18 9 63.1 1.7 21.83391
19 10 56.1 1.7 19.41176
20 10 54.9 1.5 24.40000
21 11 72.0 1.6 28.12500
22 11 NA NA NA
23 12 68.0 1.5 30.22222
24 12 68.0 1.5 30.22222
25 13 48.5 1.5 21.55556
26 13 54.0 1.5 24.00000
27 14 65.0 1.6 25.39062
28 14 64.5 1.6 25.19531
29 15 50.5 1.4 25.76531
30 15 50.1 1.5 22.26667
31 16 56.0 1.5 24.88889
32 16 55.9 1.5 24.84444
33 17 65.0 1.6 25.39062
34 17 65.0 1.6 25.39062
35 18 70.0 1.6 27.34375
36 18 71.0 1.7 24.56747
37 19 52.1 1.6 20.35156
38 19 53.0 1.5 23.55556
39 20 59.0 1.5 26.22222
40 20 59.0 1.5 26.22222
41 21 56.9 1.4 29.03061
42 21 57.1 1.5 25.37778
43 22 64.0 1.5 28.44444
44 22 63.0 1.5 28.00000
45 23 52.0 1.5 23.11111
46 23 51.0 1.5 22.66667
47 24 64.0 1.5 28.44444
48 24 64.0 1.5 28.44444
49 25 58.1 1.6 22.69531
50 25 54.0 1.6 21.09375
51 26 72.1 1.7 24.94810
52 26 68.5 1.6 26.75781
53 27 54.0 1.5 24.00000
54 27 55.0 1.5 24.44444
55 28 81.5 1.6 31.83594
56 28 70.1 1.6 27.38281
57 29 49.0 1.6 19.14062
58 29 50.1 1.6 19.57031
59 30 54.9 1.5 24.40000
60 30 56.0 1.5 24.88889
61 31 65.0 1.5 28.88889
62 31 65.0 1.5 28.88889
63 32 59.0 1.5 26.22222
64 32 58.0 1.5 25.77778
65 33 74.0 1.7 25.60554
66 33 74.5 1.7 25.77855
67 34 72.6 1.5 32.26667
68 34 76.0 1.6 29.68750
69 35 51.9 1.4 26.47959
70 35 53.0 1.4 27.04082
71 36 81.0 1.5 36.00000
72 36 NA NA NA
73 37 51.0 1.5 22.66667
74 37 51.4 1.4 26.22449
75 38 62.0 1.5 27.55556
76 38 62.5 1.5 27.77778
77 39 56.0 1.5 24.88889
78 39 56.0 1.5 24.88889
79 40 61.0 1.5 27.11111
80 40 60.0 1.5 26.66667
81 41 91.9 1.5 40.84444
82 41 92.1 1.7 31.86851
83 42 55.0 1.6 21.48437
84 42 56.0 1.6 21.87500
85 43 56.9 1.4 29.03061
86 43 57.6 1.6 22.50000
87 44 55.9 1.4 28.52041
88 44 57.0 1.6 22.26562
89 45 78.1 1.7 27.02422
90 45 NA NA NA
91 46 65.1 1.7 22.52595
92 46 65.5 1.7 22.66436
93 47 54.0 1.5 24.00000
94 47 53.0 1.5 23.55556
95 48 55.9 1.4 28.52041
96 48 54.9 1.5 24.40000
97 49 57.0 1.4 29.08163
98 49 56.8 1.5 25.24444
99 50 73.1 1.6 28.55469
100 50 71.7 1.4 36.58163
101 51 65.0 1.5 28.88889
102 51 64.9 1.5 28.84444
103 52 59.0 1.6 23.04687
104 52 54.0 1.5 24.00000
105 53 64.9 1.4 33.11224
106 53 66.0 1.5 29.33333
mutate() también remplaza columnas existentes.
Tener cuidado si se quiere reutilizar la variable original, en ese caso es mejor crear columna nueva.
Podríamos crear una columna nueva para edad en meses:
datos_fase1 %>%
select(id, age) %>%
mutate(age2 = age * 12) # Notar que se creó una columa nueva llamada age2 id age age2
1 1 33 396
2 1 32 384
3 2 27 324
4 2 27 324
5 3 25 300
6 3 25 300
7 4 37 444
8 4 38 456
9 5 31 372
10 5 32 384
11 6 38 456
12 6 38 456
13 7 26 312
14 7 26 312
15 8 34 408
16 8 34 408
17 9 30 360
18 9 30 360
19 10 38 456
20 10 38 456
21 11 37 444
22 11 36 432
23 12 33 396
24 12 33 396
25 13 25 300
26 13 25 300
27 14 33 396
28 14 33 396
29 15 37 444
30 15 37 444
31 16 30 360
32 16 30 360
33 17 40 480
34 17 40 480
35 18 36 432
36 18 36 432
37 19 35 420
38 19 35 420
39 20 22 264
40 20 22 264
41 21 29 348
42 21 29 348
43 22 41 492
44 22 41 492
45 23 27 324
46 23 27 324
47 24 25 300
48 24 25 300
49 25 37 444
50 25 37 444
51 26 32 384
52 26 32 384
53 27 31 372
54 27 31 372
55 28 40 480
56 28 40 480
57 29 23 276
58 29 23 276
59 30 31 372
60 30 31 372
61 31 38 456
62 31 38 456
63 32 38 456
64 32 38 456
65 33 41 492
66 33 41 492
67 34 40 480
68 34 40 480
69 35 30 360
70 35 30 360
71 36 39 468
72 36 39 468
73 37 30 360
74 37 30 360
75 38 37 444
76 38 37 444
77 39 23 276
78 39 23 276
79 40 20 240
80 40 20 240
81 41 31 372
82 41 31 372
83 42 39 468
84 42 39 468
85 43 36 432
86 43 36 432
87 44 28 336
88 44 28 336
89 45 35 420
90 45 35 420
91 46 36 432
92 46 36 432
93 47 35 420
94 47 35 420
95 48 27 324
96 48 27 324
97 49 35 420
98 49 35 420
99 50 28 336
100 50 28 336
101 51 41 492
102 51 41 492
103 52 34 408
104 52 34 408
105 53 33 396
106 53 33 396
La otra opción es remplazar la edad:
id age
1 1 396
2 1 384
3 2 324
4 2 324
5 3 300
6 3 300
7 4 444
8 4 456
9 5 372
10 5 384
11 6 456
12 6 456
13 7 312
14 7 312
15 8 408
16 8 408
17 9 360
18 9 360
19 10 456
20 10 456
21 11 444
22 11 432
23 12 396
24 12 396
25 13 300
26 13 300
27 14 396
28 14 396
29 15 444
30 15 444
31 16 360
32 16 360
33 17 480
34 17 480
35 18 432
36 18 432
37 19 420
38 19 420
39 20 264
40 20 264
41 21 348
42 21 348
43 22 492
44 22 492
45 23 324
46 23 324
47 24 300
48 24 300
49 25 444
50 25 444
51 26 384
52 26 384
53 27 372
54 27 372
55 28 480
56 28 480
57 29 276
58 29 276
59 30 372
60 30 372
61 31 456
62 31 456
63 32 456
64 32 456
65 33 492
66 33 492
67 34 480
68 34 480
69 35 360
70 35 360
71 36 468
72 36 468
73 37 360
74 37 360
75 38 444
76 38 444
77 39 276
78 39 276
79 40 240
80 40 240
81 41 372
82 41 372
83 42 468
84 42 468
85 43 432
86 43 432
87 44 336
88 44 336
89 45 420
90 45 420
91 46 432
92 46 432
93 47 420
94 47 420
95 48 324
96 48 324
97 49 420
98 49 420
99 50 336
100 50 336
101 51 492
102 51 492
103 52 408
104 52 408
105 53 396
106 53 396
Función de apoyo a mutate().
Categoriza variables de acuerdo a condiciones complejas
datos_fase1 %>%
select(id, age) %>%
mutate(agecat = case_when(age >= 20 & age <= 30 ~ "20-30",
age >= 31 & age <= 35 ~ "31-35",
age >= 36 & age <= 41 ~ "36-41",
TRUE ~ as.character(NA))) # Siempre cerrar con esto id age agecat
1 1 33 31-35
2 1 32 31-35
3 2 27 20-30
4 2 27 20-30
5 3 25 20-30
6 3 25 20-30
7 4 37 36-41
8 4 38 36-41
9 5 31 31-35
10 5 32 31-35
11 6 38 36-41
12 6 38 36-41
13 7 26 20-30
14 7 26 20-30
15 8 34 31-35
16 8 34 31-35
17 9 30 20-30
18 9 30 20-30
19 10 38 36-41
20 10 38 36-41
21 11 37 36-41
22 11 36 36-41
23 12 33 31-35
24 12 33 31-35
25 13 25 20-30
26 13 25 20-30
27 14 33 31-35
28 14 33 31-35
29 15 37 36-41
30 15 37 36-41
31 16 30 20-30
32 16 30 20-30
33 17 40 36-41
34 17 40 36-41
35 18 36 36-41
36 18 36 36-41
37 19 35 31-35
38 19 35 31-35
39 20 22 20-30
40 20 22 20-30
41 21 29 20-30
42 21 29 20-30
43 22 41 36-41
44 22 41 36-41
45 23 27 20-30
46 23 27 20-30
47 24 25 20-30
48 24 25 20-30
49 25 37 36-41
50 25 37 36-41
51 26 32 31-35
52 26 32 31-35
53 27 31 31-35
54 27 31 31-35
55 28 40 36-41
56 28 40 36-41
57 29 23 20-30
58 29 23 20-30
59 30 31 31-35
60 30 31 31-35
61 31 38 36-41
62 31 38 36-41
63 32 38 36-41
64 32 38 36-41
65 33 41 36-41
66 33 41 36-41
67 34 40 36-41
68 34 40 36-41
69 35 30 20-30
70 35 30 20-30
71 36 39 36-41
72 36 39 36-41
73 37 30 20-30
74 37 30 20-30
75 38 37 36-41
76 38 37 36-41
77 39 23 20-30
78 39 23 20-30
79 40 20 20-30
80 40 20 20-30
81 41 31 31-35
82 41 31 31-35
83 42 39 36-41
84 42 39 36-41
85 43 36 36-41
86 43 36 36-41
87 44 28 20-30
88 44 28 20-30
89 45 35 31-35
90 45 35 31-35
91 46 36 36-41
92 46 36 36-41
93 47 35 31-35
94 47 35 31-35
95 48 27 20-30
96 48 27 20-30
97 49 35 31-35
98 49 35 31-35
99 50 28 20-30
100 50 28 20-30
101 51 41 36-41
102 51 41 36-41
103 52 34 31-35
104 52 34 31-35
105 53 33 31-35
106 53 33 31-35
La función set_var_labels() del paquete labelled() es muy útil para etiquetar columnas.
Los datos deben tener metadatos que permitan ser legibles por el ser humano.
Esta función debería ser usada al final de todo el proceso.
Introducción a R y RStudio